Impact of Immunosuppressive Medication on the Risk of Renal Allograft Failure due to Recurrent Glomerulonephritis
Bibliographic record
Abstract
Recurrent glomerulonephritis is a major problem in kidney transplantation but the role of immunosuppression in preventing this complication is not known. We used data from the United States Renal Data System to examine the effect of immunosuppressive medication on allograft failure due to recurrent glomerulonephritis for 41,272 patients undergoing kidney transplantation from 1990 to 2003. Ten-year incidence of graft loss due to recurrent glomerulonephritis was 2.6% (95% confidence interval [CI]: 2.3-2.8%). After adjusting for important covariates, the use of cyclosporine, tacrolimus, azathioprine, mycophenolate mofetil, sirolimus or prednisone was not associated with graft failure due to recurrent glomerulonephritis. There was no difference between cyclosporine and tacrolimus or between azathioprine and mycophenolate mofetil in the risk of graft failure due to recurrent glomerulonephritis. However, any change in immunosuppression during follow-up was independently associated with graft loss due to recurrence (adjusted hazard ratio 1.30, 95% CI: 1.06-1.58, p = 0.01). In patients with a pretransplant diagnosis of glomerulonephritis, the risk of graft loss due to recurrence was not associated with any specific immunosuppressive medication. The selection of immunosuppression for kidney transplant recipients should not be made with the goal of reducing graft failure due to recurrent glomerulonephritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".